While these powerful embedding methods show excellent performance on a variety of tasks, they suffer from a major drawback. The dimensions of the vector space
used for the embedding are internal structures in a neural network and are
not comprehensible to humans. This does not present a problem when the embedding
algorithm is used as a black box.
Understanding the embedding space is, however, crucial
for several important needs, including the need to explain the decision of
a system that uses the embedding, the need to debug the embedding method and compare it to alternatives, and the need to detect biases hidden in the model [1]}, [2]}, [3]}.
